e-ISSN 2231-8542
ISSN 1511-3701
Habshah, M. and Syaiba, B. A.
Pertanika Journal of Tropical Agricultural Science, Volume 20, Issue 2, July 2012
Keywords: Maximum Likelihood Estimator, Robust Estimators, Outliers, Goodness of Fit, Monte Carlo Simulation
Published on:
It is now evident that the estimation of logistic regression parameters, using Maximum LikelihoodEstimator (MLE), suffers a huge drawback in the presence of outliers. An alternative approach is touse robust logistic regression estimators, such as Mallows type leverage dependent weights estimator(MALLOWS), Conditionally Unbiased Bounded Influence Function estimator (CUBIF), Bianco andYohai estimator (BY), and Weighted Bianco and Yohai estimator (WBY). This paper investigates therobustness of the preceding robust estimators by using real data sets and Monte Carlo simulations. Theresults indicate that the MLE behaves poorly in the presence of outliers. On the other hand, the WBYestimator is more efficient than the other existing robust estimators. Thus, it is suggested that the WBYestimator be employed when outliers are present in the data to obtain a reliable estimate.
ISSN 1511-3701
e-ISSN 2231-8542